



import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	LongformerTokenizer
			A      		=	True
			A      		=	LongformerTokenizerFast
			A      		=	True

			def      __snake_case       (self					)       ->   Optional[Any]:
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCAmelCase_:		Optional[int]							      =     [
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """\u0120""",
						    """\u0120l""",
						    """\u0120n""",
						    """\u0120lo""",
						    """\u0120low""",
						    """er""",
						    """\u0120lowest""",
						    """\u0120newer""",
						    """\u0120wider""",
						    """<unk>""",
						]
						UpperCAmelCase_:		Any							      =     dict(zip(SCREAMING_SNAKE_CASE_,   range(len(SCREAMING_SNAKE_CASE_					)					)					)					)
						UpperCAmelCase_:		Tuple							      =     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
						UpperCAmelCase_:		List[str]							      =     {"""unk_token""": """<unk>"""}

						UpperCAmelCase_:		str							      =     os.path.join(self.tmpdirname,   VOCAB_FILES_NAMES["""vocab_file"""]					)
						UpperCAmelCase_:		Any							      =     os.path.join(self.tmpdirname,   VOCAB_FILES_NAMES["""merges_file"""]					)
						with open(self.vocab_file,   """w""",   encoding="""utf-8"""					) as fp:
									fp.write(json.dumps(SCREAMING_SNAKE_CASE_					) + """\n"""					)
						with open(self.merges_file,   """w""",   encoding="""utf-8"""					) as fp:
									fp.write("""\n""".join(SCREAMING_SNAKE_CASE_					)					)

			def      __snake_case       (self,   **SCREAMING_SNAKE_CASE_					)       ->   Dict:
						kwargs.update(self.special_tokens_map					)
						return self.tokenizer_class.from_pretrained(self.tmpdirname,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   **SCREAMING_SNAKE_CASE_					)       ->   str:
						kwargs.update(self.special_tokens_map					)
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						UpperCAmelCase_:		str							      =     """lower newer"""
						UpperCAmelCase_:		Dict							      =     """lower newer"""
						return input_text, output_text

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Any							      =     self.tokenizer_class(self.vocab_file,   self.merges_file,   **self.special_tokens_map					)
						UpperCAmelCase_:		Any							      =     """lower newer"""
						UpperCAmelCase_:		int							      =     ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
						UpperCAmelCase_:		List[str]							      =     tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)  # , add_prefix_space=True)
						self.assertListEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     tokens + [tokenizer.unk_token]
						UpperCAmelCase_:		Union[str, Any]							      =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Any							      =     self.get_tokenizer()

						self.assertListEqual(tokenizer.encode("""Hello world!""",   add_special_tokens=SCREAMING_SNAKE_CASE_					),   [0, 31414, 232, 328, 2]					)
						self.assertListEqual(
						    tokenizer.encode("""Hello world! cécé herlolip 418""",   add_special_tokens=SCREAMING_SNAKE_CASE_					),   [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2],   )

			@slow
			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		List[str]							      =     self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""					)

						UpperCAmelCase_:		int							      =     tokenizer.encode("""sequence builders""",   add_special_tokens=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     tokenizer.encode("""multi-sequence build""",   add_special_tokens=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[Any]							      =     tokenizer.encode(
						    """sequence builders""",   add_special_tokens=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     tokenizer.encode(
						    """sequence builders""",   """multi-sequence build""",   add_special_tokens=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		Dict							      =     self.get_tokenizer()

						UpperCAmelCase_:		str							      =     """Encode this sequence."""
						UpperCAmelCase_:		List[str]							      =     tokenizer.byte_encoder[""" """.encode("""utf-8"""					)[0]]

						# Testing encoder arguments
						UpperCAmelCase_:		Dict							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
						self.assertNotEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		int							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
						self.assertEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						tokenizer.add_special_tokens({"""bos_token""": """<s>"""}					)
						UpperCAmelCase_:		Optional[Any]							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
						self.assertNotEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						# Testing spaces after special tokens
						UpperCAmelCase_:		List[Any]							      =     """<mask>"""
						tokenizer.add_special_tokens(
						    {"""mask_token""": AddedToken(SCREAMING_SNAKE_CASE_,   lstrip=SCREAMING_SNAKE_CASE_,   rstrip=SCREAMING_SNAKE_CASE_					)}					)  # mask token has a left space
						UpperCAmelCase_:		Tuple							      =     tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Union[str, Any]							      =     """Encode <mask> sequence"""
						UpperCAmelCase_:		Optional[int]							      =     """Encode <mask>sequence"""

						UpperCAmelCase_:		Tuple							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     encoded.index(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
						self.assertEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		str							      =     tokenizer.encode(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     encoded.index(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
						self.assertNotEqual(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Dict:
						pass

			def      __snake_case       (self					)       ->   Optional[int]:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
												UpperCAmelCase_:		Dict							      =     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[int]							      =     self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[int]							      =     """A, <mask> AllenNLP sentence."""
												UpperCAmelCase_:		Optional[Any]							      =     tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_,   return_token_type_ids=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[int]							      =     tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_,   return_token_type_ids=SCREAMING_SNAKE_CASE_					)

												# token_type_ids should put 0 everywhere
												self.assertEqual(sum(tokens_r["""token_type_ids"""]					),   sum(tokens_p["""token_type_ids"""]					)					)

												# attention_mask should put 1 everywhere, so sum over length should be 1
												self.assertEqual(
												    sum(tokens_r["""attention_mask"""]					) / len(tokens_r["""attention_mask"""]					),   sum(tokens_p["""attention_mask"""]					) / len(tokens_p["""attention_mask"""]					),   )

												UpperCAmelCase_:		Optional[int]							      =     tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
												UpperCAmelCase_:		int							      =     tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]					)

												# Rust correctly handles the space before the mask while python doesnt
												self.assertSequenceEqual(tokens_p["""input_ids"""],   [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)
												self.assertSequenceEqual(tokens_r["""input_ids"""],   [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)

												self.assertSequenceEqual(
												    SCREAMING_SNAKE_CASE_,   ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)
												self.assertSequenceEqual(
												    SCREAMING_SNAKE_CASE_,   ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						for trim_offsets, add_prefix_space in itertools.product([True, False],   repeat=2					):
									UpperCAmelCase_:		List[str]							      =     self.rust_tokenizer_class.from_pretrained(
									    self.tmpdirname,   use_fast=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_,   trim_offsets=SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		str							      =     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
									UpperCAmelCase_:		str							      =     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

									self.assertEqual(pre_tokenizer_state["""add_prefix_space"""],   SCREAMING_SNAKE_CASE_					)

									self.assertEqual(post_processor_state["""add_prefix_space"""],   SCREAMING_SNAKE_CASE_					)
									self.assertEqual(post_processor_state["""trim_offsets"""],   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   int:
						# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
						# `trim_offsets`
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
												UpperCAmelCase_:		List[Any]							      =     """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
												UpperCAmelCase_:		int							      =     f'{text_of_1_token} {text_of_1_token}'

												UpperCAmelCase_:		Optional[Any]							      =     self.rust_tokenizer_class.from_pretrained(
												    SCREAMING_SNAKE_CASE_,   use_fast=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_,   trim_offsets=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		List[str]							      =     tokenizer_r(SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
												self.assertEqual(encoding.offset_mapping[0],   (0, len(SCREAMING_SNAKE_CASE_					))					)
												self.assertEqual(
												    encoding.offset_mapping[1],   (len(SCREAMING_SNAKE_CASE_					) + 1, len(SCREAMING_SNAKE_CASE_					) + 1 + len(SCREAMING_SNAKE_CASE_					)),   )

												UpperCAmelCase_:		Tuple							      =     self.rust_tokenizer_class.from_pretrained(
												    SCREAMING_SNAKE_CASE_,   use_fast=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_,   trim_offsets=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Union[str, Any]							      =     tokenizer_r(SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
												self.assertEqual(encoding.offset_mapping[0],   (0, len(SCREAMING_SNAKE_CASE_					))					)
												self.assertEqual(
												    encoding.offset_mapping[1],   (len(SCREAMING_SNAKE_CASE_					) + 1, len(SCREAMING_SNAKE_CASE_					) + 1 + len(SCREAMING_SNAKE_CASE_					)),   )

												UpperCAmelCase_:		Optional[Any]							      =     self.rust_tokenizer_class.from_pretrained(
												    SCREAMING_SNAKE_CASE_,   use_fast=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_,   trim_offsets=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Dict							      =     tokenizer_r(SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
												self.assertEqual(encoding.offset_mapping[0],   (0, len(SCREAMING_SNAKE_CASE_					))					)
												self.assertEqual(
												    encoding.offset_mapping[1],   (len(SCREAMING_SNAKE_CASE_					), len(SCREAMING_SNAKE_CASE_					) + 1 + len(SCREAMING_SNAKE_CASE_					)),   )

												UpperCAmelCase_:		List[str]							      =     self.rust_tokenizer_class.from_pretrained(
												    SCREAMING_SNAKE_CASE_,   use_fast=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_,   trim_offsets=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[Any]							      =     tokenizer_r(SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
												self.assertEqual(encoding.offset_mapping[0],   (0, len(SCREAMING_SNAKE_CASE_					))					)
												self.assertEqual(
												    encoding.offset_mapping[1],   (len(SCREAMING_SNAKE_CASE_					), len(SCREAMING_SNAKE_CASE_					) + 1 + len(SCREAMING_SNAKE_CASE_					)),   )

												UpperCAmelCase_:		str							      =     f' {text}'

												# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
												#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
												# )
												# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
												# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
												# self.assertEqual(
												#     encoding.offset_mapping[1],
												#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
												# )

												UpperCAmelCase_:		int							      =     self.rust_tokenizer_class.from_pretrained(
												    SCREAMING_SNAKE_CASE_,   use_fast=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_,   trim_offsets=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Union[str, Any]							      =     tokenizer_r(SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
												self.assertEqual(encoding.offset_mapping[0],   (1, 1 + len(SCREAMING_SNAKE_CASE_					))					)
												self.assertEqual(
												    encoding.offset_mapping[1],   (1 + len(SCREAMING_SNAKE_CASE_					) + 1, 1 + len(SCREAMING_SNAKE_CASE_					) + 1 + len(SCREAMING_SNAKE_CASE_					)),   )

												UpperCAmelCase_:		str							      =     self.rust_tokenizer_class.from_pretrained(
												    SCREAMING_SNAKE_CASE_,   use_fast=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_,   trim_offsets=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[int]							      =     tokenizer_r(SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
												self.assertEqual(encoding.offset_mapping[0],   (0, 1 + len(SCREAMING_SNAKE_CASE_					))					)
												self.assertEqual(
												    encoding.offset_mapping[1],   (1 + len(SCREAMING_SNAKE_CASE_					), 1 + len(SCREAMING_SNAKE_CASE_					) + 1 + len(SCREAMING_SNAKE_CASE_					)),   )

												UpperCAmelCase_:		List[Any]							      =     self.rust_tokenizer_class.from_pretrained(
												    SCREAMING_SNAKE_CASE_,   use_fast=SCREAMING_SNAKE_CASE_,   add_prefix_space=SCREAMING_SNAKE_CASE_,   trim_offsets=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		str							      =     tokenizer_r(SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_					)
												self.assertEqual(encoding.offset_mapping[0],   (0, 1 + len(SCREAMING_SNAKE_CASE_					))					)
												self.assertEqual(
												    encoding.offset_mapping[1],   (1 + len(SCREAMING_SNAKE_CASE_					), 1 + len(SCREAMING_SNAKE_CASE_					) + 1 + len(SCREAMING_SNAKE_CASE_					)),   )











import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a      :  int			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     """huggingface/label-files"""
			UpperCAmelCase_:		Optional[int]							      =     """imagenet-1k-id2label.json"""
			UpperCAmelCase_:		Union[str, Any]							      =     json.load(open(hf_hub_download(lowerCAmelCase__		,						lowerCAmelCase__		,						repo_type="""dataset"""  )		,						"""r"""  )  )
			UpperCAmelCase_:		Optional[Any]							      =     {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
			UpperCAmelCase_:		Tuple							      =     {v: k for k, v in idalabel.items()}

			UpperCAmelCase_:		int							      =     """std_conv""" if """bit""" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			UpperCAmelCase_:		Any							      =     BitConfig(
			    conv_layer=lowerCAmelCase__		,						num_labels=1_0_0_0		,						idalabel=lowerCAmelCase__		,						labelaid=lowerCAmelCase__		,						)

			return config







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]  ):





			"""simple docstring"""


			if "stem.conv" in name:
						UpperCAmelCase_:		Dict							      =     name.replace("""stem.conv"""		,						"""bit.embedder.convolution"""  )
			if "blocks" in name:
						UpperCAmelCase_:		Tuple							      =     name.replace("""blocks"""		,						"""layers"""  )
			if "head.fc" in name:
						UpperCAmelCase_:		int							      =     name.replace("""head.fc"""		,						"""classifier.1"""  )
			if name.startswith("""norm"""  ):
						UpperCAmelCase_:		List[Any]							      =     """bit.""" + name
			if "bit" not in name and "classifier" not in name:
						UpperCAmelCase_:		str							      =     """bit.encoder.""" + name

			return name







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase_:		Optional[int]							      =     Image.open(requests.get(lowerCAmelCase__		,						stream=lowerCAmelCase__  ).raw  )
			return im







@torch.no_grad()
def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		str=False  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     get_config(lowerCAmelCase__  )

			# load original model from timm
			UpperCAmelCase_:		str							      =     create_model(lowerCAmelCase__		,						pretrained=lowerCAmelCase__  )
			timm_model.eval()

			# load state_dict of original model
			UpperCAmelCase_:		Optional[Any]							      =     timm_model.state_dict()
			for key in state_dict.copy().keys():
						UpperCAmelCase_:		Any							      =     state_dict.pop(lowerCAmelCase__  )
						UpperCAmelCase_:		Optional[Any]							      =     val.squeeze() if """head""" in key else val

			# load HuggingFace model
			UpperCAmelCase_:		Union[str, Any]							      =     BitForImageClassification(lowerCAmelCase__  )
			model.eval()
			model.load_state_dict(lowerCAmelCase__  )

			# create image processor
			UpperCAmelCase_:		Any							      =     create_transform(**resolve_data_config({}		,						model=lowerCAmelCase__  )  )
			UpperCAmelCase_:		List[Any]							      =     transform.transforms

			UpperCAmelCase_:		Tuple							      =     {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			UpperCAmelCase_:		int							      =     BitImageProcessor(
			    do_resize=lowerCAmelCase__		,						size={"""shortest_edge""": timm_transforms[0].size}		,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,						do_center_crop=lowerCAmelCase__		,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}		,						do_normalize=lowerCAmelCase__		,						image_mean=timm_transforms[-1].mean.tolist()		,						image_std=timm_transforms[-1].std.tolist()		,						)

			UpperCAmelCase_:		Optional[Any]							      =     prepare_img()
			UpperCAmelCase_:		Tuple							      =     transform(lowerCAmelCase__  ).unsqueeze(0  )
			UpperCAmelCase_:		Dict							      =     processor(lowerCAmelCase__		,						return_tensors="""pt"""  ).pixel_values

			# verify pixel values
			assert torch.allclose(lowerCAmelCase__		,						lowerCAmelCase__  )

			# verify logits
			with torch.no_grad():
						UpperCAmelCase_:		Dict							      =     model(lowerCAmelCase__  )
						UpperCAmelCase_:		str							      =     outputs.logits

			print("""Logits:"""		,						logits[0, :3]  )
			print("""Predicted class:"""		,						model.config.idalabel[logits.argmax(-1  ).item()]  )
			UpperCAmelCase_:		List[str]							      =     timm_model(lowerCAmelCase__  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(lowerCAmelCase__		,						outputs.logits		,						atol=1e-3  )
			print("""Looks ok!"""  )

			if pytorch_dump_folder_path is not None:
						Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
						print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'  )
						model.save_pretrained(lowerCAmelCase__  )
						processor.save_pretrained(lowerCAmelCase__  )

			if push_to_hub:
						print(F'Pushing model {model_name} and processor to the hub'  )
						model.push_to_hub(F'ybelkada/{model_name}'  )
						processor.push_to_hub(F'ybelkada/{model_name}'  )


if __name__ == "__main__":
	a      :  Dict			     =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='resnetv2_50x1_bitm',
	    type=str,
	    help='Name of the BiT timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether to push the model to the hub.',
	)

	a      :  Tuple			     =			parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






